Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …

Benefiting from the ability to process and integrate data from various modalities, multi-modal
foundation models (FMs) facilitate potential applications across a range of fields, including …

The 6th generation mobile network (6G) is expected to be launched in the early 2030s. The
architecture of 6G will be the convergence of space, air, ground, and undersea networks …

The 5 G networks are effectively deployed worldwide, and academia and industries have
begun looking at 6 G network communication technology for consumer electronics …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

The Internet of Vehicles (IoV) has become a fundamental platform for advancing Intelligent
Transportation Systems (ITSs) and Intelligent Connected Vehicles (ICVs). However, the …